PDF(Pubmed)
Abstract：
UNASSIGNED: No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.
UNASSIGNED: Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.
UNASSIGNED: Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).
UNASSIGNED: A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.
UNASSIGNED: The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.
UNASSIGNED: The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.
UNASSIGNED: Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.
UNASSIGNED: Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.
UNASSIGNED: Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).
UNASSIGNED: A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.
UNASSIGNED: The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.
UNASSIGNED: The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.
UNASSIGNED: Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.
摘要：
未经证实：目前还没有确定的生物标志物用于抗VEGF治疗新生血管性年龄相关性黄斑变性（nAMD）的疗效和持久性。这项研究评估了基于放射学的定量OCT生物标志物，这些生物标志物可以预测抗VEGF治疗的反应和持久性。
UNASSIGNED：使用机器学习（ML）分类器评估基线生物标志物以预测抗VEGF治疗的耐受性。
未经评估：来自OSPREY研究的81名接受治疗的nAMD参与者，包括15名超级应答者(达到并维持视网膜液分辨率的患者)和66名非超级应答者(未达到或维持视网膜液分辨率的患者)。
UNASSIGNED：从流体中提取了总共962个基于纹理的放射学特征，视网膜下高反射材料（SHRM），和OCT扫描的不同视网膜组织区室。前8个特点，通过最小冗余最大相关性特征选择方法选择，在交叉验证的方法中使用4个ML分类器进行评估，以区分2个患者组。还进行了基线和第3个月之间不同基于纹理的放射学描述符(δ-纹理特征)变化的纵向评估,以评估它们与治疗反应的关联。此外,8基线临床参数和基线OCT的组合，三角洲纹理特征，并通过交叉验证的方法评估了临床参数与治疗反应的相关性.
UNASSIGNED：受试者工作特征曲线（AUC）下的交叉验证面积，准确度,灵敏度,并计算特异性以验证分类器的性能。
UNASSIGNED：使用基于纹理的基线OCT特征，二次判别分析分类器的交叉验证AUC为0.75±0.09。基线和第3个月之间不同OCT区室内的δ-纹理特征产生0.78±0.08的AUC。基线临床参数视网膜下色素上皮体积和视网膜内液体积产生0.62±0.07的AUC。当所有的基线，delta,和临床特征相结合，分类器性能的统计显着提高(AUC，获得0.81±0.07）。
UNASSIGNED：基于放射组学的OCT图像定量评估显示可区分nAMD中抗VEGF治疗的超应答者和非超应答者。发现基线流体和SHRM三角洲纹理特征在各组之间最具区别。
UNASSIGNED：使用机器学习（ML）分类器评估基线生物标志物以预测抗VEGF治疗的耐受性。
未经评估：来自OSPREY研究的81名接受治疗的nAMD参与者，包括15名超级应答者(达到并维持视网膜液分辨率的患者)和66名非超级应答者(未达到或维持视网膜液分辨率的患者)。
UNASSIGNED：从流体中提取了总共962个基于纹理的放射学特征，视网膜下高反射材料（SHRM），和OCT扫描的不同视网膜组织区室。前8个特点，通过最小冗余最大相关性特征选择方法选择，在交叉验证的方法中使用4个ML分类器进行评估，以区分2个患者组。还进行了基线和第3个月之间不同基于纹理的放射学描述符(δ-纹理特征)变化的纵向评估,以评估它们与治疗反应的关联。此外,8基线临床参数和基线OCT的组合，三角洲纹理特征，并通过交叉验证的方法评估了临床参数与治疗反应的相关性.
UNASSIGNED：受试者工作特征曲线（AUC）下的交叉验证面积，准确度,灵敏度,并计算特异性以验证分类器的性能。
UNASSIGNED：使用基于纹理的基线OCT特征，二次判别分析分类器的交叉验证AUC为0.75±0.09。基线和第3个月之间不同OCT区室内的δ-纹理特征产生0.78±0.08的AUC。基线临床参数视网膜下色素上皮体积和视网膜内液体积产生0.62±0.07的AUC。当所有的基线，delta,和临床特征相结合，分类器性能的统计显着提高(AUC，获得0.81±0.07）。
UNASSIGNED：基于放射组学的OCT图像定量评估显示可区分nAMD中抗VEGF治疗的超应答者和非超应答者。发现基线流体和SHRM三角洲纹理特征在各组之间最具区别。
